Lane-level vehicle navigation for vehicle routing and traffic management

ABSTRACT

A lane-level vehicle routing and navigation apparatus includes a simulation module that performs microsimulation of individual vehicles in a traffic stream, and a lane-level optimizer that evaluates conditions along the candidate paths from an origin to a destination as determined by the simulation module, and determines recommended lane-level maneuvers along the candidate paths. A link-level optimizer may determines the candidate paths based on link travel times determined by the simulation module. The simulation may be based on real-time traffic condition data. Recommended candidate paths may be provided to delivery or service or emergency response vehicles, or used for evacuation planning, or to route vehicles such as garbage or postal trucks, or snowplows. Corresponding methods also may be used for traffic planning and management, including determining, based on microsimulation, at least one of (a) altered road geometry, (b) altered traffic signal settings, such as traffic signal timing, or (c) road pricing.

CROSS REFERENCE TO RELATED APPLICATION

This claims the benefit of, commonly-assigned U.S. Provisional PatentApplication No. 61/789,019, filed Mar. 15, 2013, which is herebyincorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION

This invention provides processes and apparatus for providing lane-levelroute guidance for individual vehicles of various types throughtransportation networks for making trips between widely separated triporigins and destinations and methods for managing regional and corridortraffic at the lane level through measurement, simulation, andoptimization to identify effective routing strategies for one, some,and/or very large numbers of diverse motor vehicles. The invention alsoprovides for improved methods and apparatus for traffic planning andmanagement as well as traffic simulation and prediction.

There are great inefficiencies in the usage of road networks that comefrom poor choice of routes and lanes by individual drivers, leading tocongestion and delay for both the individual driver and large numbers ofindividuals traveling in other vehicles. Emergency vehicles struggle toget to their destinations and often are not certain of the best routesto take or which lanes of travel would be the fastest for their mission.Known navigation and traffic management systems that could potentiallyidentify and implement congestion-relieving guidance are primitive andmay even be counter-productive when the same information is given to alldrivers.

SUMMARY OF THE INVENTION

A lane-level vehicle routing and navigation apparatus according toembodiments of the invention includes a simulation module that performsmicrosimulation of individual vehicles in a traffic stream, and alane-level route optimizer that evaluates predicted conditions alongcandidate paths from an origin to a destination as determined by thesimulation module, and determines recommended lanes to use and theassociated lane-level maneuvers along the candidate paths. A link-leveloptimizer may be used to determine the candidate paths based on linktravel times determined by the simulation module which then may befurther refined with the lane-level optimizer. The simulation may bebased on real-time traffic condition data.

The lane-level optimizer may take account of the operation of trafficcontrols along the candidate paths, including simulation of the trafficcontrols or real-time traffic control data. The simulation may bemulti-threaded and/or distributed for faster computation in order toprovide more timely navigation guidance or to evaluate multiplealternatives simultaneously.

In accordance with another embodiment of the invention, a lane-levelvehicle routing and navigation apparatus includes a simulation modulethat performs microsimulation of individual vehicles in a traffic streamto recommend candidate paths at lane level, and inputs for real-timetraffic condition data, wherein the microsimulation is based at least inpart on said real-time traffic condition data.

Candidate lane-level paths may be provided to emergency responsevehicles, or used for evacuation planning, or to route vehicles whichseek to traverse entire groups of road segments, such as garbage or mailtrucks, or snowplows.

Methods in accordance with the invention are also provided. The methodsalso may be used for traffic planning or management, includingdetermining, based on microsimulation, at least one of (a) improvementsto road geometry, or (b) traffic signal settings and improvements totraffic signal timing.

The invention includes improvements to traffic microsimulation methodsby taking account of more realistic lane level trajectory selectionsmade by drivers. Within the context of a single simulation run, a lookahead mechanism is used to identify better lane-level guidance andensure that the guidance is feasible in light of other traffic in latertime intervals. The look ahead mechanism also improves other aspects oftraffic microsimulation.

These methods can be especially effective in future autonomous vehiclesystems that require lane-level guidance that is appropriate in light ofother traffic and that further enable lane-level navigation through theability to locate vehicles by their lane of travel.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 is a visual representation of a microsimulation of traffic inPhoenix, Ariz.;

FIG. 2 shows a traffic environment in which embodiments of the inventionmay operate;

FIG. 3 shows two candidate paths between two points;

FIG. 4 shows greater detail of a portion of FIG. 3;

FIG. 5 shows greater detail of a portion of FIG. 4;

FIG. 6 shows an example of a traffic condition leading to ananticipatory lane change;

FIG. 7 is a flow diagram of a navigation engine according to embodimentsof the invention;

FIG. 8 is an example of a road segment;

FIG. 9 is a lane graph representing the road segment of FIG. 8;

FIG. 10 is a diagram illustrating multi-threading of a simulation; and

FIG. 11 is diagram illustrating clustering of computers performing asimulation.

DETAILED DESCRIPTION OF THE INVENTION

Existing navigation systems provide route guidance primarily by choosingroutes based upon expected link travel times and turn penalties, andoccasionally preferences for use of certain types or classes of roads.These systems can readily identify the locations where mandatory lanechanges are required to traverse a particular recommended or chosenpath, but are not sensitive to the best lanes to use in various trafficsituations and in different locations. They also fail to representbarriers and restrictions on lane use and lane changes explicitly. Noneconsiders traffic congestion at the lane level explicitly, or recognizesthe differences in driver behavior for choosing lanes or evenalternative paths when certain lanes are congested or inaccessible. Withlane-level data, more effective routes and lane choices can bedetermined. Processes that exploit lane-level detail in route guidancenavigation can be further evaluated and also refined through appropriatetraffic simulation.

The prevailing view has been that mesoscopic traffic simulation, whichlacks lane-level modeling detail, is the only feasible method ofproviding real-time route guidance because microscopic simulation modelshave not yet been built at a large enough geographic scale, and would betoo expensive to build and validate or too computationally demanding tobe useful. Obstacles to region-wide traffic microsimulation havetraditionally included lack of detailed data on trip patterns by time ofday and network performance by road segment and the lanes therein,difficulties in modeling driver route choice, problems in accuratesimulation of large numbers of vehicles especially under heavilycongested traffic conditions, as well as the computational burden ofperforming the simulation within a short enough time for the results tobe useful. However, microsimulation at the regional scale can beimplemented using methods described in commonly-assigned U.S. Pat. No.7,155,376, which is hereby incorporated by reference herein in itsentirety.

In traffic simulation, and particularly in microscopic trafficsimulation in which vehicles are simulated with lane-level detail, routeguidance can be provided to vehicles while at the same time consideringother traffic on the network. Yet existing simulation-based systemsattempt to model actual driver behavior and discretionary lane changingwithout regard to normative strategies for lane use from an origin to adestination or a portion of a route. Consequently, these systems cannotproperly determine the best lanes to use for an entire trip or a portionof a trip or the locations within road links at which to attempt or makelane changes based upon historical, measured, simulated, or forecastedlane-level densities and possible speeds.

Current commercially available navigation systems, and navigationguidance provided by Internet sources, can frequently identifycongestion as it takes place and provide alternative routes. Thisguidance has three fundamental limitations. First, all travelers betweenthe same two locations are given the same guidance which itself can becounter-productive. Second, and more seriously, it is assumed thatcurrent conditions will continue to prevail long enough for the routeguidance to be pertinent. This may seldom be the case as congestion maybe worsening or lessening at any point in time. Third, there is nospecific guidance regarding which lanes in specific locations wouldprovide better expected travel times than other lanes.

Systems have been proposed that rely on historical data at the lanelevel, or lane-level probe data, or both, but neither historical norcurrent data may apply at all to the travel conditions that will beexperienced at some point in time. Changes in real world conditions suchas weather or special events or accidents will render such prior datamisleading. Also, typically historical data or probe data will not beavailable for all lane segments and movements through intersections.While statistical methods such as autoregressive models that have beenproposed might be used to fill in missing observations, those methodsstill have the same limitations of reliance upon the past experience andconditions.

There may be other limitations as well such as the failure to capturethe effects of traffic signals or blocked intersections, double-parkedvehicles, and pedestrians, as well as failure to consider thevariability of the travel environment which may render guidance based onaverage conditions considerably sub-optimal.

Adaptive traffic signal systems and other measures like ramp-meteringcan lead to different performance of the traffic system at differentcongestion levels. Failure to represent the behavior and impact of thesemechanisms can lead to faulty or sub-optimal route guidance.

Explicit consideration of other vehicles and the mix of other vehiculartraffic can be advantageous in routing and traffic management. It iswell-understood that the presence of even one or a few slow vehicles candrastically and stochastically impact overall traffic flow and causecongestion. Also, the percentage of trucks, especially large trucks, isknown to have a significant impact on network performance. Withoutvehicle-level modeling, the presence of slow vehicles could not be takeninto account.

New forms of data collection from crowd-sourcing, mobile phones, aerialimagery, LIDAR, more precise Global Positioning System devices, roadsensors, on-board sensors, and road cameras are gradually lowering thedata barriers for region-wide simulation. While not all of the new datasources have lane-level geographic resolution and precision, the supplyof lane-level information available for routing, traffic simulation, andtraffic management purposes should be expected to increase.

Current navigation systems treat time coarsely, if at all, whenproviding route guidance. There may be expected link travel times orspeeds that are used that correspond to peak periods and other times ofthe day or they may simply use some composite of travel times based uponposted speeds and/or speeds observed in real time. Typically thesespeeds vary by the functional class of a road, but are not collected orobserved for each single road link in a region or beyond. Some systemscan make use of real-time data on congestion, but these data aretypically restricted to the main highways and cannot take account ofconditions on arterials or on the conditions that will prevail more than15-30 minutes into the future. None of these systems use lane-levelinformation explicitly for routing, except possibly to indicate how manylanes are provided for each turn when following a route.

Existing traffic management systems, even the most advanced real-timetraffic management and control systems, use simplified models forprediction. Travel demand and travel times are either based onhistorical data or limited real-time measurements. There is no evidencefor the estimation of lane-level travel demand and optimization oftraffic flow based on such demand.

In one known system that uses a mesoscopic simulation method, the supplyside traffic performance is treated at the link level and not at thelane level. Generally speaking, the research literature has taught thatmicrosimulation cannot be feasible for entire regions and cannot becomputed quickly enough to provide navigation guidance.

However, above-incorporated U.S. Pat. No. 7,155,376 describes alane-level GIS database suitable for routing and trafficmicrosimulation. Based on that system, a working microsimulation modelof all of the traffic flowing within a 500 square mile area of Phoenix,Ariz. has been developed, and even on a laptop computer, this simulationruns faster than real time. With additional enhancement, further speedimprovements can be achieved making use of the system for navigation areality. Methods and apparatus provided herein generate more detailedand more efficient route guidance for one vehicle—a single-occupantautomobile, a carpool vehicle (which may have additional availablelane-use alternatives such as high-occupancy vehicle (HOV) lanes), atruck, a bus, a motorcycle, or a bicycle—and also provide for managementand amelioration of traffic congestion that results from a plurality ofvehicles traversing paths from widely-separated origins and destinationswithin a large metropolitan region. Lane-use permissions andrestrictions for different vehicle types (such as the carpool case notedabove) also may be taken into account in lane-level routing.

These improved processes are based on the ability to store, manage, andutilize time-dependent, lane-level information on traffic and geometricconditions on highways and between and within intersections on streets.Between intersections, a road may be divided into segments based ontraffic characteristics (e.g., curvature, grade, number of lanes), andeach segment may be divided into lanes. Alignments between upstream anddownstream lanes at adjacent segments and intersections are described bylane connectors. Travel demand, either as individual trips, or asaggregated trip counts by entities, are associated geographically andtemporally to the network using origins, destinations, and desireddeparture or arrival time. Vehicle locations and trajectories,lane-segment-specific vehicle lane-occupancies/densities, vehicle gapdistributions, and speeds can be recorded for short time intervals fromobservations and measurements and also from detailed microsimulation.These data can be used to analyze the likely travel times and othercharacteristics of potential vehicle trajectories at the lane-level froman origin to a destination. Similarly, these data items can be gatheredfor the components of trajectories that take place inside ofintersections where delays are often experienced due to conflictingmovements of vehicles and even pedestrians. If not all of the necessarydata can be observed or measured in the field, detailed simulation canbe used to supplement the available measurements for the neededlocations and/or time intervals.

FIG. 1 depicts the aforementioned microsimulation of traffic in Phoenix,Ariz., in which box 1 represents an intersection, including vehicles andlane-level geometry, with a near-ground-truth level of detail, as shownin box 2. The model also includes detailed traffic signal timing andother traffic management data, as shown in box 3, enabling the accuratesimulation of signal operations and drivers' behavioral responses tothem. For example, box 4 illustrates the operational state of a trafficsignal controller at an arbitrary time t, and box 5 represents theoperational state of that controller at time t+10 seconds. This allowswith high-fidelity simulation of both the demand (i.e., vehicles) andsupply (i.e., lane-level geometries and traffic signal operations)elements of the environment in which a navigation system operates.

Lane level navigation problems abound even in small sections ofmetropolitan road networks due to their complex nature, a lack ofinformation available to drivers and/or traffic management systems andstaff, and insufficient analytical and predictive methods. The followingexamples may be illustrative.

Example 1

First, consider a junction between two major highways at a time of daywhen both roads are congested. On one of the highways, traffic hasspilled back more than two miles on the lane designated for exiting fromthe one highway to the other highway. The three other lanes are movingbut slowly and there are numerous lane changes being made by fidgetydrivers. A driver needs to exit. Should that driver move into the exitlane at the end of the long queue, or travel in the next-rightmost laneand then force his/her way into the exit lane? If the latter, at whatlocation should the lane change be attempted?

Example 2

In a two-lane roundabout, a driver needs to enter at one point and exitat the third exit point. What lanes should be used and to what extent?

Example 3

A work zone has blocked the right lane of a road. How far ahead ofworking area should signs be placed, and when should a driver in a laneto be closed start his/her maneuver into the open lanes?

Example 4

There is a lane drop coming. When and where should a driver attempt tomove out of the lane that is ending? This will depend upon the extantconditions including those predicted for some number of minutes. Driverswho are familiar with the road will know that the lane drop is present,while others such as tourists will not. How should the system accountfor both informed and uninformed drivers?

Example 5

The highway ahead is blocked but there is an unblocked exit ramp fromthe highway. There is a longer alternative route from the exit ramparound the congested area. Will it save time to leave the highway ratherthan stay on it? It is not clear how long the blockage will take toclear and it is not clear how many will take the alternative route anddegrade its performance.

Generally discretionary lane changes involve anticipation of improveddriving experience in the target lanes. Changing lanes in congestedtraffic involves delay and causes delay to others making minimization ofindividual delay and overall system delay difficult to predict.

For example, it is difficult to know the state of a traffic signal or atoll plaza fifteen minutes in the future. It would be unreasonable tothink that the state would be the same as it was one day earlier or onthe same day in the previous week, even at the same time of day.However, a comprehensive traffic simulation system capable ofrepresenting the elements and complexities of the traffic environmentlike that depicted in FIG. 2 can assimilate historical information andreal-time traffic information available from the field and then relatethat information into useful short-term predictions and, thus,meaningful lane-level route guidance.

FIG. 2 illustrates the context in which systems according to embodimentsof the invention may operate. Hardware commonly found in the field—forexample, magnetic loop detectors 201 embedded in pavement and trafficcameras 202—can provide real-time traffic data, which can be transmittedover electrical or optical cable, or wirelessly, to a centralizedcomputing environment 203, where historical traffic data are alsostored. Traffic signal controllers 204 also provide real-time stateinformation for remote performance monitoring. Satellite and terrestrialwireless communication devices 205 support the transmission of trafficdata to the computing environment 203, where simulations are run androuting strategies are tested before guidance is sent back to travelersthrough those same communication devices. Drivers yet to depart, and enroute, may receive guidance on personal computers 206 at home or work,or on on-board systems 207, or on mobile devices 208 (includinghand-held navigation devices as well as mobile telephones). Instructions209 can also be returned to the traffic signal control devices 204 tobetter manage traffic. As a result, various users of the system (e.g.,drivers of commercial vehicles, passenger automobiles, etc.) may receivelane-level route guidance 210 that is tailored to their particular needor objective.

A navigation engine that uses the lane-level information along with amore elaborate network graph makes it possible to generate recommendedlane-level routes. The guidance takes into account the time and effortspent in changing lanes as well as expected operant speeds in lanes andat and through intersections (whether signalized or unsignalized), orexpected speeds in merging and weaving sections and lanes on highways.These can be measured from observations of various types and verified orvalidated through application of Highway Capacity Manual methods or moredetailed simulation. Microsimulation can be used to generate orsupplement the data if the data are not available from other sources orcannot be cost-effectively gathered.

A navigation engine that simulates regional traffic flow at very shorttime intervals ranging from 0.1 seconds to 1 second can be used toevaluate alternative lane-level routes. In one embodiment, a simulatedprobe vehicle is used to calculate the performance of a particularlane-level trajectory. Multiple simulation runs make it possible toestimate the variance in travel time associated with any giventrajectory and to consider the variance and thus the reliability of thelane-level route. Any lane-level route choice will be subject tonumerous stochastic influences that arise from traffic signals and theinherently variable nature of traffic flows. Navigation systems couldconsider reliability in addition to expected travel time in recommendingroutes to travelers.

Route selection for navigation has traditionally used some form ofshortest path calculation using widely-known and deeply-researchedalgorithms for correct and quick calculation of the best path. Thesemethods are applied at the link level using node to node or link to linkshortest path methods. If suitable data can be obtained, the very samemethods can be applied at the lane-level, although the size of thenetwork will be very large for a metropolitan region and keeping trackof the results is more complex because transition between a pair oflanes on a path may occur anywhere along the path, rather than only atparticular points along the lane. There are also route choice modelsusing discrete choice formulations than can be extended to considerlane-level details.

Link-level and lane-level shortest paths with various objectivefunctions can provide recommendations for lane-level navigation or beused to generate candidates for more refined lane-level navigation. Theparadigm of shortest path calculation has a resolution limit of the linkor link segment and does not lend itself to the possibility ofcontinuous lane change possibilities within links. This limitationcannot be profitably addressed through creating more numerous, shorterlinks which would, in of itself, create other problems in terms of dataneeds and data validity.

Through microsimulation, candidate lane-level shortest-path trajectoriescan be evaluated with alternative and intelligently perturbedlane-changing locations and timing. Thus the simulation can be used torefine and select among lane-level guidance alternatives.

A geographically accurate lane-level network database as well asreal-time measurement and computer-synthesized traffic information areused in the simulation. Because the volume of data can be substantial,the navigation network and level of detail used must be selectedcarefully to reduce the computational and communication burden.

Anticipatory, route-strategic, planned lane changes can improve upontravelers travel times and routes in a significant way. FIG. 3 shows twoof candidate paths 301, 302 from location 311 to location 313. In thiscase, a driver has chosen the hatched path and is approaching location312. The distance or number of links (i.e., road segments) downstream onthe chosen path for which a driver must plan near-term lane changingdecisions is called the look-ahead range. The immediate downstream roadsegments connecting the next two intersections are highlighted at 303and shown in greater detail in FIG. 4.

The lane connections and traffic conditions at the lane level areimportant information for a good navigation system. In our example, thedriver of vehicle 401 intends to make a left turn at intersection 403,where the two left-lanes are allowed to make the left turn. If laneconnection is of concern, then the driver should move into the left-mostlane at the current location in order to stay on path. However, thedriver may delay the lane change until passing the first intersection402 if a large number of vehicles 411 are making a left turn atintersection 402 and there is a queue spilling back for that turningmovement. The lane-level navigation system and its supporting database,recognizing this pattern of congestion in the appropriate lane-levelcontext, and acknowledging certain driver behaviors, is capable ofoptimizing the path at the lane level and guiding the driveraccordingly.

Based on the road type, number of lanes, geography of the road, andexistence of traffic signals and signs, embodiments of the invention canautomatically generate the lane connectivity in typical urbanintersection and freeway interchange configurations. The result of theautomatically generated lane connections can be validated and furtherrevised if necessary using survey data, aerial imagery, and vehicletrajectory data. As an example, FIG. 5 illustrates the lane connectionat intersection 403 in more detail. The curved lines with arrowheadsinside the intersection are called lane connectors. Lane connectorsindicate the geometric alignments and permitted movements between lanes.As features in the lane-level database described, the lane connectorssupport the storage of data indicating the tendency, or probability, ofdrivers to use the particular connector. As shown, solid lane connectors501 are more commonly used than dashed lane connectors 502.

FIG. 6 shows another example 600 of an anticipatory lane change. Thisexample involves a limited-access highway 601. The eastbound off-ramp602 has a queue 612 of vehicles spilling back because of traffic volumesexceeding capacity at a downstream intersection 603. Drivers intendingto take the next off-ramp 604, who might otherwise have moved rightbefore off-ramp 602, would be advised to use the center and left lanes605, 606, rather than join queue 612 forming at off-ramp 602,particularly if the off-ramp 604 is further downstream or there existmultiple lane connectors to off-ramp 604, thereby avoiding unnecessaryqueuing and enjoying a shorter travel time. On the other hand, if thereis a higher probability of missing the optimal and intended route (e.g.,because off-ramp 604 is very close to off-ramp 602), or the cost ofusing the alternative route is excessive, then an earlier lane change,even though it results in joining queue 612, may constitute the bestcourse of action and would be recommended to the driver.

One element of the lane-level navigation system is the inclusion of alane-level route optimizer which can be used in addition to or insteadof a link-level optimizer. Given the origin, destination and vehicletype and preference (e.g., high occupancy vehicle (HOV), car vs. truck,value of time, etc.), the link-level optimizer computes candidate pathsrepresented by a sub-network or a veritable “hammock” of link sequencesconnecting an origin and a destination. A hammock can be thought of as anetwork of links and nodes emanating from a single link at the origin,or the vehicle's current position, and converging at a single link atthe destination. According to a predefined look-ahead threshold(preference), the lane-level optimizer evaluates the travel time, delayand driving experience on path candidates using either historicalmeasurements of lane-level traffic density and speed, ormicrosimulation. The result of this evaluation is then used to derivethe lane-level route guidance, and in some cases (e.g., none of the lanechange maneuvers are possible or desirable) may also invoke thelink-level optimizer to recompute the path candidates according to therevised criteria. In the microsimulation employed by the lane-leveloptimizer, traffic signals (which may be adaptive traffic signals) atdownstream intersections (and other traffic controls, including, e.g.,message signs and other driver information systems) are simulated todetermine intersection delay realistically as are vehicle trajectoriesand conflicts inside of intersections. It should be noted that in someembodiments, the origin, destination and vehicle type and preference,and any other inputs, can be entered directly into the lane-leveloptimizer, without using a link-level optimizer at all.

The navigation engine 700 can be illustrated in the diagram in FIG. 7.In navigation engine 700, historical speed data 701 are used to populatea geographic database and model 702 representing the region-wide network(4). Records in the database represent road segments, lanes, and laneconnectors as described. Together with historical speed data 701, thenetwork is used to establish temporal and spatial patterns of trafficflow and congestion. Known traffic signal timing and management data 703and estimates of travel demand 704, either measured or calibrated usinghistorical data (e.g., traffic counts), also are stored with the networkmodel and used to run simulations. Additionally, real-time data 705,including speed measurements from mobile telephones, detectors, andcameras, and incident reports and timing data 706, for example from atraffic management center, are used to feed current information to thenetwork.

With lane-level model 702 of the region-wide network using thehistorical and real-time information, accurate dynamic trafficassignments and simulations are run at 707 to produce predictions oftravel times and turning movement delays 708 that in turn drive alink-level route optimizer 709. A set of alternative link pathsrepresented by a hammock 710 connecting a traveler's origin to his/herdestination is produced by optimizer 709. In one embodiment, a set oflane graphs 711 is developed for the traveler. Ideally, the lane graphsencompass the entirety of the optimal link route, but if computationalresources are not sufficient, the lane graph may be limited to alook-ahead range, as shown.

Based on the lane graph, an optimal lane path is produced at lane-leveloptimizer 712 and transmitted to the traveler in the form of lanechanging and turning movement route guidance instructions 713. If thetraveler changes lanes at 714, engine 700 determines at 715 whether thevehicle is still in the set of lanes deemed by the lane graph to beconnected to the optimal lane path. If the traveler is still in a“connected” lane, lane-level optimizer 712 updates the instructions tothe traveler based on the current lane. Otherwise, engine 700 returns tothe link-level optimizer 709.

When it is detected at 716 that the traveler has moved to a new roadsegment, engine 700 determines at 717 whether the traveler is on a roadsegment on the current advised link path, and if so the lane graph 711is updated to reflect the new position in the network. Otherwise, theengine returns to the link-level optimizer 709. In this way, thetraveler's location in relation to the region-wide network iscontinually tracked by engine 700, which uses simulation coupled withlink-level optimizer 709 and lane-level optimizer 712 to guide thetraveler.

In an embodiment in which link-level optimizer 709 is omitted, alllink-level and lane-level computations are performed in the lane-leveloptimizer. That includes any recomputations required based on lane orsegment changes.

In navigation engine 700, possible lane-level trajectories are evaluatedby looking ahead at the traffic in lanes downstream on alternativeroutes by constructing a lane-level network or graph of variable sizefor only the part of routes within a predefined look-ahead range, but itcan also potentially consider all lane-level paths from a location tothe vehicle's destination if available computation and/or communicationcapacity permit.

A lane-level network (otherwise known as a lane graph) is a model torepresent the detailed information on the lane use regulation, lanechange and lane alignment in the road network. It is specificallydesigned to support lane-level navigation and microsimulation of vehiclemovements. The design of the lane graph can be best shown by asimplified example as shown in FIG. 8.

FIG. 8 shows a stretch of eastbound freeway 800 including ten roadsegments (length not to scale). In this example, segments 801-807 arefreeway, segments 808 and 809 are off-ramps, and segment 810 is anon-ramp. Two left-most lanes are designate as being restricted to highoccupancy/toll (HOT) use only, where lane changes between HOT andgeneral-purpose (GP) lanes are allowed (shown by dashed lines) insegment 801 but prohibited (shown by solid line between second and thirdlanes 811, 812) in segments 802-807. While a HOT driver may use alllanes, which lane the driver should be using may depend on his/herdestination and the chosen route. On the other hand, because of theexistence of a barrier and/or lane-change regulation, which route thedriver should be taking also depends on which lane he/she is currentlyin. The optimal lane-level path further depends on the trafficconditions on the HOT and GP lanes. The lane graph model of thisinvention is designed to streamline the operation of lane-leveloptimizer.

As an example, FIG. 9 is part of lane graph 900 for a driver travelingin upstream segment 1. The graph contains vertices (representing lanes)and edges (representing relations between lanes). Depending whether adriver is going to exit 1 (808), exit 2 (809), or further downstreamthrough the mainline, some of the vertices that represent the lanes canbe trimmed (e.g., removed from the driver's intended route) or morevertices can be added.

In the lane graph 900 of FIG. 9, each vertex (circle) represents a lanewithin a specific road segment. The black vertices 901 are lanesrestricted for HOT vehicles; while other vertices 902 represent thelanes which have no lane use restriction.

A vertical line 903 with double arrow between a pairs of verticesrepresents lane change maneuvers between the neighbor lanes. If the lanechange is allowed only in one direction (e.g., left-to-right, but notright-to-left, or vice versa), then a single arrow would be drawnbetween a pair of adjacent lanes.

Each horizontal or diagonal line 904 in lane graph 900 represents a laneconnector in the network. Lane connectors describe the alignment betweenupstream and downstream lanes. If more than one such line 904 connectsmultiple upstream lane vertices to a downstream lane vertex, then itrepresents a merge; if more than one such line 904 connects one upstreamlane vertex to multiple downstream lane vertices, then it represent lanesplit (i.e., divergence) 914.

The numbers (1 or 0) on each line 904 represent the cost assigned to thelane change maneuver. With these costs assigned, computing a lane-levelpath from driver's current position to any downstream link can beperformed by well-established shortest path algorithms. The minimum costof the path gives the minimum number of lane changes required for thedriver to stay on his/her current path. By tracking the predecessor ofeach vertex along the shortest path, it can be determined where a lanechange should take place and its distance from the current position. Byapplying techniques such as depth-first or breadth-first search on lanegraph 900, alternative lane paths from the current position (i.e.,origin) to a chosen downstream destination can be enumerated.

Similarly, travel speed, delay, traffic density, and gaps betweenvehicles for the lanes in the graph can be recorded as the attributesfor lane vertices in the graph. The possibilities and difficulties oflane change maneuver can be represented by the attributes of edges thatconnect neighbor lanes in the graph. Searching techniques, therefore,can be performed on the lane graph to evaluate the cost and drivingexperience along a candidate lane path and select the optimal locationto start the required lane changes.

In this framework, a lane path, either that representing a vehicle'stravel diary up to the current position or that representing lane-levelguidance derived from the foregoing techniques, can be represented bygeographic coordinates and events or by a sequence of data triplets.Each data triplet could contain a (1) unique road segment identifier(i.e., a column of vertices in the lane graph), a (2) lane positionindex in the segment (i.e., a row of vertices in the lane graph), and(3) the position of a lane change expressed in terms of relativedistance from either end of the lane. When formulating a lane path forrecommendation to the driver, the distance reflects the location atwhich a lane change is advised, allowing for a comfortable intervalbefore which the lane change should be completed. Advisory lane changepositions can be derived from current or historical speed data, whichare used to characterize the degree of difficulty of a lane change at agiven position. When providing guidance, the data triplets could beconverted to geographic coordinates and event notices and be coordinatedwith real-time GPS information on the vehicle's current location.

Lane changes (or the act of staying in the current lane when no lanechange should be made) can be thought of as being of two types:mandatory and discretionary. Mandatory lane changes refer to the casesin which a lane change must be made in order to make a turn that isrequired to remain on a given route as defined at the link level; whilediscretionary lane changes refer to the lane change maneuvers in orderto improve the driving experience such as to gain speed or to pass aheavy vehicle, etc. When there exist multiple lane connections,mandatory lane changes may reduce the choice set (i.e., alternativetrajectory paths) of discretionary lane changes.

One can further distinguish two types of discretionary lane changes. Thefirst of these is based upon the immediate context of the link or linksegment and the lanes therein or those immediately downstream. Thiscorresponds to known simulators which have short look-ahead processesthat select lanes while keeping vehicles on their routes as defined bylink sequences. In this type of discretionary lane changing, decisionsare inherently myopic and based upon the alternative lanes available ineach route segment or perhaps on a few links downstream.

A second type of discretionary lane change is route-strategic and hasthe goal of improving the travel time or other figure of merit for theremainder or other significant portion of the route by focusing on thestrategic choice of lane selections and the locations for lane-changing.This process is performed by adding and/or removing the lane verticesand edges in the lane graph. It can also be adjusted over the course ofmultiple simulation runs as the system learns which behaviors scenariosare superior in terms of the lane-level trajectory.

The reduction in delay and gain in speed associated with differing lanechange locations can be further considered in computing the bestlane-level path using methods well-known to those versed in the state ofthe practice of vehicle navigation systems and shortest path algorithms.These can be particularized to the location within the link or linksegment and consider stochastic expected travel times and lane changeprobabilities.

Based on a combination of data from historical measurement and real-timemeasurement, numerous simulation runs can be used to synthesize andpredict the likely travel time and traffic density along the alternativepaths at the lane-level. The result of these simulation runs are thenprocessed and used to evaluate the likely travel times for differentlane-level routings and the recommended lane change locations. Sincelane changes cannot be guaranteed at a specific location, navigationrecommendations can include a first location at which to attempt arecommended lane change. Navigation instructions can also describe inadvance a set of lane maneuvers that are recommended for the vehicle.

The recommended routes are dynamically determined based upon the timeand location of the vehicle, and so can be optimized for the trafficconditions that will exist and be experienced during the duration of thevehicle's trip. Thus, accidents and work zones can be taken into accountin the navigation guidance.

The prediction function and simulation provide capabilities over andabove the use of historical or even real-time conditions as they moreaccurately represent the situation that a vehicle will find itself inthe future portion of its trip.

The simulation engines used by link-level optimizer 709 and lanelane-level optimizer 712 can be customized to reflect driver preferencesincluding those for the maximum speed, lane preferences, simplicity ofroute, value of time, desirable time window of the arrival, and vehicleclassification associated to lane use restrictions and toll fees, ifany. The simulation engine can also take adaptive traffic signaloptimization into account when and where it is present.

Link-level optimizer 709 generates link-level alternative routes. It canprovide multiple alternative routes from a driver's current position tothe desired destination. These alternative routes can be formulated as asub-network or “hammock” of a directional sequence of links. For thechosen route, as the guided vehicle move from one road segment to nextor changes lane, the downstream portion of the links along the routewithin a look-ahead range is then expanded to a lane-level network orgraph 711 as presented earlier. Lane-level optimizer 712 is used togenerate possible lane-level trajectories. These lane-level alternativepaths are then evaluated to determine the optimal one based on presetrules and criteria.

With the alternative routes determined first, the communication betweenan in-vehicle navigation device and a remote server can be kept minimal.This allows the navigation engine 700 to interact with real-time datafeeds on lane conditions and queues and give adaptive route guidance.

Multiple simulation runs can be further utilized to refine or establishthe robustness of any route recommendation. These simulation runs cangive an idea of the likelihood that a given route will be traversable inthe expected minimum time. For choosing the best route for a singlevehicle trip, the simulation can evaluate likely scenarios under currentconditions and provide a range of effective alternatives. Dynamictraffic assignment (DTA) and simulation can be performed on the serverside, or in a remote distributed computing environment, to estimate thenetwork state, which considers reliability as well as the expectedtravel time. Historical speed profiles, real-time measurements of speedand travel time, information on traffic management and control, andtravel demand estimates and traffic incidents all feed into the networkstate estimator and predictor and are used to determine current andfuture speed, travel time, and delay on the links and turning movementsin the region-wide network.

Generally, a dynamic user equilibrium traffic assignment model may beused to model traffic. In this formulation, an iterative solution may becomputed such that at each iteration, vehicles choose their best pathbased upon a prior iteration's predicted travel times by time period onthe network. At the computed approximated equilibrium solution, thetravel times on the network by time interval have stopped changing andthe route choices will therefore have stabilized. In a preferredembodiment, a microsimulation-based DTA will be used for providing routeand lane-level navigation guidance. This will be derived fromidentifying the best trajectory for a single vehicle making a tripbetween a specific origin and specific destination at a given departuretime. Also, various events can be factored into the simulation andcomputation of the best lane-level trajectory such as an accident or aroad modification due to road construction or maintenance. Simulationmakes it possible to incorporate the future effects that cannot bemeasured contemporaneously or known from history.

Lane drops and unanticipated incidents and work zones lead to increasedcongestion. The system described uses traffic simulation to estimate thetraffic condition upstream of the events (incidents and work zones) andthe amount of delay. It guides the driver to determine the optimalposition of lane changes that not only optimize the individual's drivingexperiences, but that make better use of the capacity available toincrease the throughput at these bottlenecks. Lane-level trafficmanagement has the potential to improve public safety by reducingaccidents as well as reducing delay and energy consumption associatedwith vehicular traffic.

The lane-level guidance can lead to differences from the link-levelshortest path. While this is clearly evident in the earlier examplewhere barriers exist between lanes or the number of required lanechanges is too many to maneuver safely at the prevailing speed withinthe distance available, it is less obvious when the contributing factorsare the traffic pattern and the difference in traffic conditions acrosslanes. At the link level, path calculation has to be performed on somekind of average of the travel time or generalized cost. However,vehicles in a traffic stream are often traveling to differentdestinations, and traffic densities and lane utilization across roadsegments are not necessary even or balanced. Furthermore, a driver'svalue of time and perception of delay may differ. Thus, the shortestpath based on mean travel time, turning delay, and/or generalized costfor an “average” driver may not necessarily be the optimal path for aspecific driver. In the described system, the need for individualism ofdriver's navigation is explicitly considered by taking into account thevehicle's location and the varying traffic conditions across lanes.

As with many route guidance systems already available, the systemdescribed may include the link-level route calculation. Here, however,that calculation is only the first step for screening route candidatesin a wide-area. The calculation could also serve as a data reductionstep so the more detailed lane-level path evaluation is needed only fora much smaller data set, so that the computation and communicationrequirement is minimized.

In one particular embodiment, expected travel times are used andminimized, but other aspects of the trip could be included in theobjective to be optimized. This includes, but is not limited to, tollsincurred for the use of certain roads or lanes or for entering arestricted area, the number of lane changes required and the remainingdistance within which the lane changes must be performed, the number ofcontrolled (e.g., signalized, stop sign-controlled) intersections, thepresence of parked vehicles, vehicles entering from and leaving for sidestreets and driveways, and the prevalence of pedestrian traffic.

Improvements in the accuracy of GPS systems, laser and radar rangefinders, microwave, image processing and object recognition, may providethe ability to locate vehicles reliably in lanes and at locations withinintersections and could be used to feed the navigation engine asdescribed. Expansion of the capacity of wireless communication withlower costs may enable the described system to send thelocation-specific traffic data and request for navigation to a centralserver, which runs the traffic prediction using simulation and generatesoptimized lane-level path and navigation instructions on an individualbasis.

Since the simulation and vehicle routing is at the lane-level, it canenable the traffic controller to conduct optimization of signal timing,phasing and coordination based on predicted performance measures such asdelays, throughput, number of stops, and percentage of vehicles arrivingat green signals. Such optimization may either be performed off-lineusing software that utilizes the described method, or by components inthe controller to perform online signal optimization via real-timecommunication with the approaching vehicles and estimates of the patternof vehicle arrivals.

The described lane-level routing method may also assist self-drivingautomated vehicles to make appropriate lane-change decisions. Ratherthan using predefined paths, the automated vehicle can continuouslyevaluate its alternative lane paths, choose the optimal route and makenecessary lane changes to stay on the prescribed path.

The navigation engine may variously be run on a cluster of computersthat serves results over the Internet, on in-vehicle computers, ondedicated navigation devices, on smart telephones, or on other similarlycapable computing devices that may be developed. Running multipleinstances simultaneously may result in faster and better predictions.

While microscopic simulation is used to provide lane-level performanceand routing, some portions of the network could be simulated by coarsermacroscopic or mesoscopic or a hybrid combination with microscopicmeans. Due to the computational burden involved, the simulation wouldtypically be multi-threaded and also distributed. The multi-threading ofthe simulation of vehicle movement and network state update can beimproved by using a node adjacency matrix to assign tasks to each threadsuch that all the active threads are processing nodes that are notadjacent to one another and the maximum number of threads is put towork. This method avoids the certain locking of threads needed forwrite-access of shared data items. As shown in the diagram in FIG. 10,if the white nodes 1001 are the ones actively processed by eightthreads, then the vehicle movements managed by these threads can besafely performed without locking the update of location-specificvariables. This method also helps to balance the workload among thethreads because any free thread is immediately put to work (unless thenetwork is too small compared to the number of threads available and nonon-adjacent nodes exist that are ready for processing).

The simulation can also be distributed to a cluster of multiplecomputers based on network decomposition, which minimizes the number ofboundary links between sub-networks and balances the load among thecomputers in the cluster. By minimizing the thread-locking on eachcomputer responsible for a sub-network, and limiting the amount ofrequired communication between the computers that jointly perform theregion-wide simulation, on-time computational performance of the networkstate estimation and prediction can be achieved. Because the trafficmeasurement and network events occur in real time, the system operatesin a rolling horizon style. For every 5, 10, 15, or 30 minutes, as thenew measurements and events data become available, a new cluster ofcomputers is activated to perform network state estimation and trafficprediction for the next time period. The diagram in FIG. 11 illustrateshow three clusters 1101, 1102, 1103 of computers take turns in rotationto perform distributed travel time estimation and traffic prediction fordifferent time periods. Each computer in a class simulates part of thenetwork based on network decomposition.

Once a new set of network state estimations and traffic predictionsbecomes available, the link-level route optimizer is put to work forsubscribed drivers. The link-level route optimizer computes alternativeroutes at the current time from each driver's current position tohis/her desired destination. As explained earlier, the alternative routecan be described as a sub-network or as a hammock of sequences ofdirectional links. Based on an individual driver's specific preference,these alternative routes are evaluated to compute a performance measureand to provide route guidance based on current conditions.

Given a desired route or a set of alternative routes, a lane graph maybe constructed for the portion of route within a look-ahead range. Thisallows more detailed microsimulation to be performed and data to beassembled only for a much smaller and selected area of the network. Thetask of the lane-level path optimizer, therefore, is to evaluate thealternative trajectories or lane paths to estimate the travel time anddelay and compute an index of driving experience. The best alternativeis then recommended as navigation guidance.

Lane changes are not always possible at the optimal desired location.Lane changes require the presence of suitable gaps in the target laneappropriate for the speed of travel and the driver's aggressiveness. Thesystem described can assess the difficulty of lane changes and recommendearlier or later lane changes based upon measured or predicted laneoccupancies. Whether a lane change should be recommend earlier or latercan be user defined. Automated learning from the driving habit of thespecific user is included in the system so that the navigation guidancewill be tailored to driving behavior and expectation.

Consistency in route guidance and in simulated traffic may not bereadily achievable by available techniques without enhancement ormodification. In dynamic (time-dependent) best path calculationsestimates are required of the link or lane travel times in future timeintervals. Yet even if these are taken from previously simulated trafficconditions, those estimates may be flawed or inconsistent with theconditions for which and at the time that the route guidance is firstproduced or later updated.

Verification of lane-level guidance can be verified by performingmicrosimulations and assessing the consequences.

A particular application of embodiments of the invention is in routingof emergency response vehicles. In conditions of emergency response, thenormal driving behavior of vehicles in the traffic stream is altered bytheir response to sirens and visual detection of emergency responsevehicles. While these responses and behavioral compliance can beobserved from time to time, the specific consequences will depend uponroad geometry and traffic levels and these can be simulated withmicrosimulation. Similarly, the behavioral model in the trafficsimulation for the emergency vehicles themselves can reflect theirtarget speeds and the ability to use lanes in the opposing direction andwillingness to ignore traffic signals to reach their destinations asquickly as possible. Here too, lane-level routing strategies may easilyoutperform drivers' conventional knowledge of shortest link-level routesleading to further time benefits. Similarly, these systems and methodscan be used to determine evacuation routes in case of emergency. Themodel for driver behavior can be modified to reflect how drivers behaveor might behave under evacuation conditions and in response to guidanceprovided to the public.

Another application of lane-level routing is for commercial service ordelivery vehicles and also for snow plows and garbage trucks. Thesevehicles have different sizes and performance characteristics and oftenwill have to navigate road segments that have legally and illegallyparked vehicles that naturally affect their choice of lanes and stoppinglocations.

In the known case of arc routing such as that appropriate for postaldeliveries and garbage collection, where entire groups of streetsegments must be traversed as opposed to point-to-point deliveryoperations, certain lane choices will be required and not discretionary.This can be accommodated in traffic microsimulation including simulationof the stopping behavior and specific delivery or collection activitywhich may ultimately lead to a better and more realistic allocation ofworkload to each vehicle and a better and more realistic estimate of thetime required to perform the assigned tasks.

In the case of snow plowing, lane-level routing can be even moreeffective as decisions can be taken to open at least one lane on majorroads and the choice of the lane-level overall route plan and trajectoryfor each vehicle can be more expertly planned, simulated, and carriedout. Similarly, the plowing route can be designed to avoid completelymissing smaller roads.

In all of these cases of routing of commercial and public vehicles, thepresence of other traffic and parked vehicles can be represented moreappropriately than in conventional routing systems that lack lane-leveland vehicle-level detail.

While microscopic traffic simulation of the type described can providelane-level navigation guidance for individual vehicles, it also offersimproved capabilities for traffic management. In traffic management,strategies are implemented to smooth traffic flow. These strategies canbe pre-determined for various conditions or be responsive to real-timeand emerging conditions.

With respect to traffic management strategies, it is insufficient tofocus on improving the performance for a single vehicle. Providingtraffic management information to a plurality of vehicles throughmessage signs and other communications can easily be done, but assessingeffective strategies requires a more complex analysis as drivers adjustto emerging traffic conditions and newly provided information aboutthose conditions. Region-wide dynamic traffic assignment that determinessome form of equilibrium condition on the network can provide the neededpredictions and be used to evaluate variations in traffic managementstrategies including tolling and more sophisticated measures such asdynamic road pricing.

The described system can be used as an engine to estimate theanticipatory travel times by a free public system or by private serviceprovider(s) who sell traffic information and route guidance tosubscribers. By collecting how users respond to the travel timeinformation and route guidance, the system can include a self-tuningcomponent to adjust its parameters such that route guidance will becomemore accurate as the system accumulates more data and learns from users'response over time.

The system can also be used to improve the performance of bus systems byselecting lane-level routing strategies for buses that are responsive tothe overall traffic environment. Bus priority and/or signal preemptionstrategies, which are often lane-level in design or intent, can also bereadily incorporated. Other modes of travel can also be incorporated andhave lane-level guidance computed for them.

Thus, lane-level vehicle routing and navigation apparatus, andcorresponding methods, have been provided. One skilled in the art willappreciate that the present invention can be practiced by other than thedescribed embodiments, which are presented for purposes of illustrationand not of limitation, and the present invention is limited only by theclaims which follow.

What is claimed is:
 1. A lane-level vehicle routing and navigationapparatus comprising: a simulation module that performsmicrosimulation-based dynamic traffic assignment for individual vehiclesin a traffic stream to predict travel times; a link-level optimizer thatdetermines candidate paths based on link travel times predicted by saidsimulation module; and a lane-level route optimizer that evaluatespredicted conditions along candidate paths from an origin to adestination as determined by said simulation module, and determinesrecommended lanes to use and the associated lane-level maneuvers alongthe candidate paths; wherein: said apparatus provides lane userecommendations from said lane-level route optimizer to a vehicleoperator via a user communication device.
 2. The lane-level vehiclerouting and navigation apparatus of claim 1 wherein: said simulationmodule performs multiple simulation runs for at least some of saidcandidate paths as part of said microsimulation-based dynamic trafficassignment; and said link-level optimizer is utilized to identifytime-efficient and reliable routes and route guidance.
 3. The lane-levelvehicle routing and navigation apparatus of claim 2 wherein saidsimulation module performs said multiple simulation runs at intervals ofup to 1 second.
 4. The lane-level vehicle routing and navigationapparatus of claim 3 wherein said simulation module performs saidmultiple simulation runs at intervals of between 0.1 second and 1second.
 5. The lane-level vehicle routing and navigation apparatus ofclaim 4 wherein said simulation module performs said multiple simulationruns at intervals of 0.1 second.
 6. The lane-level vehicle routing andnavigation apparatus of claim 1 wherein the recommended lanes to use andthe associated lane-level maneuvers take into account driver routepreferences.
 7. The lane-level vehicle routing and navigation apparatusof claim 6 wherein said driver route preferences comprise lanepreferences.
 8. The lane-level vehicle routing and navigation apparatusof claim 1 wherein said lane-level route optimizer takes account of theoperation of traffic controls along the candidate paths.
 9. Thelane-level vehicle routing and navigation apparatus of claim 8 whereinsaid operation of said traffic controls is simulated by said simulationmodule.
 10. The lane-level vehicle routing and navigation apparatus ofclaim 8 wherein said operation of said traffic controls is evaluatedbased on real-time traffic control data.
 11. The lane-level vehiclerouting and navigation apparatus of claim 1 further comprising: inputsfor real-time traffic condition data; wherein: said simulation modulebases said microsimulation-based dynamic traffic assignment at least inpart on said real-time traffic condition data.
 12. The lane-levelvehicle routing and navigation apparatus of claim 1 wherein saidlane-level optimizer takes account of future downstream lane conditions.13. The lane-level vehicle routing and navigation apparatus of claim 1wherein models used in said microsimulation, and said lane-leveloptimizer, take account of effects of obstacles to traffic flow.
 14. Thelane-level vehicle routing and navigation apparatus of claim 13 whereinsaid obstacles to traffic flow comprise one or more of (a) work zones,or (b) traffic accidents.
 15. The lane-level vehicle routing andnavigation apparatus of claim 1 wherein different routings are providedto different vehicles traveling from said origin to said destination.16. The lane-level vehicle routing and navigation apparatus of claim 1wherein said simulation module is at least one of (a) multi-threaded, or(b) distributed.
 17. A lane-level vehicle routing and navigationapparatus comprising: a simulation module that performsmicrosimulation-based dynamic traffic assignment for individual vehiclesin a traffic stream, along candidate paths determined by link-leveloptimization, to predict travel times and to make lane userecommendations; and inputs for real-time traffic condition data;wherein: said simulation module bases said microsimulation-based dynamictraffic assignment at least in part on said real-time traffic conditiondata; said apparatus further comprising: a user communication devicethat provides said lane use recommendations to a vehicle operator. 18.The lane-level vehicle routing and navigation apparatus of claim 17wherein said real-time traffic condition data are available for at leastone of (a) one or more vehicles, or (b) one or more road segments. 19.The lane-level vehicle routing and navigation apparatus of claim 17wherein said real-time traffic condition data are available at lanelevel.
 20. The lane-level vehicle routing and navigation apparatus ofclaim 17 wherein said simulation module performs microsimulations forsaid microsimulation-based dynamic traffic assignment at intervals of upto 1 second.
 21. The lane-level vehicle routing and navigation apparatusof claim 20 wherein said simulation module performs saidmicrosimulations for said microsimulation-based dynamic trafficassignment at intervals of between 0.1 second and 1 second.
 22. Thelane-level vehicle routing and navigation apparatus of claim 21 whereinsaid simulation module performs said microsimulations for saidmicrosimulation-based dynamic traffic assignment at intervals of 0.1second.
 23. A lane-level vehicle routing and navigation methodcomprising: performing microsimulation-based dynamic traffic assignment,in a simulation engine, of individual vehicles in a traffic stream topredict travel times; determining, in a link-optimizing engine,candidate paths from an origin to a destination based on predicted linktravel times determined by said performing; evaluating, in alane-optimizing engine, predicted conditions along said candidate pathsfrom said origin to said destination as determined by said performing,and determining recommended lane-level maneuvers along said candidatepaths; and providing lane use recommendations, from said evaluating insaid lane-optimizing engine, to a vehicle operator via a usercommunication device.
 24. The lane-level vehicle routing and navigationmethod of claim 23 wherein: said performing comprises performingmultiple simulation runs for at least some of said candidate paths aspart of said microsimulation-based dynamic traffic assignment; and saiddetermining comprises identifying time-efficient and reliable routes androute guidance.
 25. The lane-level vehicle routing and navigation methodof claim 24 further comprising: inputting real-time traffic conditiondata; wherein: said performing bases said microsimulation-based dynamictraffic assignment at least in part on said real-time traffic conditiondata.
 26. The lane-level vehicle routing and navigation method of claim24 wherein said microsimulations for performing microsimulation-baseddynamic traffic assignment occur at intervals of up to 1 second.
 27. Thelane-level vehicle routing and navigation method of claim 26 whereinsaid microsimulations for performing microsimulation-based dynamictraffic assignment occur at intervals of between 0.1 second and 1second.
 28. The lane-level vehicle routing and navigation method ofclaim 27 wherein said microsimulations for performingmicrosimulation-based dynamic traffic assignment occur at intervals of0.1 second.
 29. The lane-level vehicle routing and navigation method ofclaim 23 wherein the determining takes into account driver routepreferences.
 30. The lane-level vehicle routing and navigation method ofclaim 29 wherein said driver route preferences comprise lanepreferences.
 31. The lane-level vehicle routing and navigation method ofclaim 23 wherein said determining takes account of the operation oftraffic controls along the candidate paths.
 32. The lane-level vehiclerouting and navigation method of claim 31 wherein said performingcomprises simulating said operation of said traffic controls.
 33. Thelane-level vehicle routing and navigation method of claim 31 whereinsaid simulating said operation of said traffic controls is based onreal-time traffic control data.
 34. The lane-level vehicle routing andnavigation method of claim 23 wherein said determining takes account ofpredicted downstream lane conditions.
 35. The lane-level vehicle routingand navigation method of claim 23 wherein models used in saidperforming, and said determining, take account of effects of obstaclesto traffic flow.
 36. The lane-level vehicle routing and navigationmethod of claim 35 wherein said obstacles to traffic flow comprise oneor more of (a) work zones, or (b) traffic accidents.
 37. The lane-levelvehicle routing and navigation method of claim 23 wherein differentroutings are provided to different vehicles from said origin to saiddestination.
 38. A lane-level vehicle routing and navigation methodcomprising: performing, in a simulation engine, microsimulation-baseddynamic traffic assignment for individual vehicles in a traffic stream,to predict travel times along candidate paths determined by link-leveloptimization, and to make lane use recommendations; inputting real-timetraffic condition data; and providing said lane use recommendations,from said performing microsimulation-based dynamic traffic assignment insaid simulation engine, to a vehicle operator via a user communicationdevice; wherein: said performing bases said microsimulation-baseddynamic traffic assignment at least in part on said real-time trafficcondition data.
 39. The lane-level vehicle routing and navigation methodof claim 38 wherein said real-time traffic condition data are availablefor at least one of (a) one or more vehicles, or (b) one or more roadsegments.
 40. The lane-level vehicle routing and navigation method ofclaim 38 wherein said real-time traffic condition data are available atlane level.
 41. The lane-level vehicle routing and navigation method ofclaim 38 wherein microsimulations for said performingmicrosimulation-based dynamic traffic assignment occur at intervals ofup to 1 second to make said lane use recommendations.
 42. The lane-levelvehicle routing and navigation method of claim 41 wherein saidmicrosimulations for performing microsimulation-based dynamic trafficassignment occur at intervals of between 0.1 second and 1 second. 43.The lane-level vehicle routing and navigation method of claim 42 whereinsaid microsimulations for performing microsimulation-based dynamictraffic assignment occur at intervals of 0.1 second.
 44. A lane-levelvehicle routing and navigation apparatus comprising: a simulation modulethat performs microsimulation-based dynamic traffic assignment forindividual vehicles in a traffic stream at intervals of up to 1 secondto predict travel times; a link-level optimizer that determinescandidate paths from an origin to a destination based on link traveltimes predicted by said simulation module; and a lane-level routeoptimizer that evaluates predicted conditions along said candidate pathsfrom said origin to said destination as determined by said simulationmodule, and determines recommended lanes to use and the associatedlane-level maneuvers along said candidate paths; wherein: said apparatusprovides lane use recommendations from said lane-level route optimizerto a vehicle operator via a user communication device.
 45. Thelane-level vehicle routing and navigation apparatus of claim 44 whereinsaid simulation module performs microsimulations for saidmicrosimulation-based dynamic traffic assignment at intervals of between0.1 second and 1 second.
 46. The lane-level vehicle routing andnavigation apparatus of claim 45 wherein said simulation module performssaid microsimulations for said microsimulation-based dynamic trafficassignment at intervals of 0.1 second.
 47. The lane-level vehiclerouting and navigation apparatus of claim 44 wherein: said simulationmodule performs multiple simulation runs for at least some of saidcandidate paths for said microsimulation-based dynamic trafficassignment; and said link-level optimizer is utilized to identifytime-efficient and reliable routes and route guidance.
 48. Thelane-level vehicle routing and navigation apparatus of claim 44 whereinthe recommended lanes to use and the associated lane-level maneuverstake into account driver route preferences.
 49. The lane-level vehiclerouting and navigation apparatus of claim 48 wherein said driver routepreferences comprise lane preferences.
 50. The lane-level vehiclerouting and navigation apparatus of claim 44 wherein said lane-levelroute optimizer takes account of the operation of traffic controls alongthe candidate paths.
 51. The lane-level vehicle routing and navigationapparatus of claim 50 wherein said operation of said traffic controls issimulated by said simulation module.
 52. The lane-level vehicle routingand navigation apparatus of claim 50 wherein said operation of saidtraffic controls is evaluated based on real-time traffic control data.53. The lane-level vehicle routing and navigation apparatus of claim 44further comprising: inputs for real-time traffic condition data;wherein: said simulation module bases said microsimulation-based dynamictraffic assignment at least in part on said real-time traffic conditiondata.
 54. The lane-level vehicle routing and navigation apparatus ofclaim 44 wherein said lane-level optimizer takes account of futuredownstream lane conditions.
 55. The lane-level vehicle routing andnavigation apparatus of claim 44 wherein models used in saidmicrosimulation-based dynamic traffic assignment, and said lane-leveloptimizer, take account of effects of obstacles to traffic flow.
 56. Thelane-level vehicle routing and navigation apparatus of claim 55 whereinsaid obstacles to traffic flow comprise one or more of (a) work zones,or (b) traffic accidents.
 57. The lane-level vehicle routing andnavigation apparatus of claim 44 wherein different routings are providedto different vehicles traveling from said origin to said destination.58. The lane-level vehicle routing and navigation apparatus of claim 44wherein said simulation module is at least one of (a) multi-threaded, or(b) distributed.
 59. A lane-level vehicle routing and navigationapparatus comprising: a simulation module that performsmicrosimulation-based dynamic traffic assignment for individual vehiclesin a traffic stream, at intervals of up to 1 second, along candidatepaths determined by link-level optimization, to predict travel times andto make lane use recommendations; and inputs for real-time trafficcondition data; wherein: said simulation module bases saidmicrosimulation-based dynamic traffic assignment at least in part onsaid real-time traffic condition data; said apparatus furthercomprising: a user communication device that provides said lane userecommendations to a vehicle operator.
 60. The lane-level vehiclerouting and navigation apparatus of claim 59 wherein said simulationmodule performs said microsimulation-based dynamic traffic assignment atintervals of between 0.1 second and 1 second.
 61. The lane-level vehiclerouting and navigation apparatus of claim 60 wherein said simulationmodule performs said microsimulation-based dynamic traffic assignment atintervals of 0.1 second.
 62. The lane-level vehicle routing andnavigation apparatus of claim 59 wherein said real-time trafficcondition data are available for at least one of (a) one or morevehicles, or (b) one or more road segments.
 63. The lane-level vehiclerouting and navigation apparatus of claim 59 wherein said real-timetraffic condition data are available at lane level.
 64. A lane-levelvehicle routing and navigation method comprising: performing, in asimulation engine, at intervals of up to 1 second, microsimulation-baseddynamic traffic assignment for individual vehicles in a traffic streamto predict travel times; determining, in a link-optimizing engine,candidate paths from an origin to a destination based on link traveltimes predicted by said performing; evaluating, in a lane-optimizingengine, predicted conditions along said candidate paths from said originto said destination as determined by said performing, and determiningrecommended lane-level maneuvers along the candidate paths; andproviding lane use recommendations, from said evaluating in saidlane-optimizing engine, to a vehicle operator via a user communicationdevice.
 65. The lane-level vehicle routing and navigation method ofclaim 64 wherein said performing comprises performing saidmicrosimulation-based dynamic traffic assignment at intervals of between0.1 second and 1 second.
 66. The lane-level vehicle routing andnavigation method of claim 65 wherein said performing comprisesperforming said microsimulation-based dynamic traffic assignment atintervals of 0.1 second.
 67. The lane-level vehicle routing andnavigation method of claim 64 wherein: said performing comprisesperforming multiple simulation runs for at least some of said candidatepaths for said microsimulation-based dynamic traffic assignment; andsaid determining comprises identifying time-efficient and reliableroutes and route guidance.
 68. The lane-level vehicle routing andnavigation method of claim 64 wherein the determining takes into accountdriver route preferences.
 69. The lane-level vehicle routing andnavigation method of claim 68 wherein said driver route preferencescomprise lane preferences.
 70. The lane-level vehicle routing andnavigation method of claim 64 wherein said determining takes account ofthe operation of traffic controls along the candidate paths.
 71. Thelane-level vehicle routing and navigation method of claim 70 whereinsaid performing comprises simulating said operation of said trafficcontrols.
 72. The lane-level vehicle routing and navigation method ofclaim 70 wherein said simulating said operation of said traffic controlsis based on real-time traffic control data.
 73. The lane-level vehiclerouting and navigation method of claim 64 further comprising: inputtingreal-time traffic condition data; wherein: said performing bases saidmicrosimulation-based dynamic traffic assignment at least in part onsaid real-time traffic condition data.
 74. The lane-level vehiclerouting and navigation method of claim 64 wherein said determining takesaccount of future downstream lane conditions.
 75. The lane-level vehiclerouting and navigation method of claim 64 wherein models used in saidperforming, and said determining, take account of effects of obstaclesto traffic flow.
 76. The lane-level vehicle routing and navigationmethod of claim 75 wherein said obstacles to traffic flow comprise oneor more of (a) work zones, or (b) traffic accidents.
 77. The lane-levelvehicle routing and navigation method of claim 64 wherein differentroutings are provided to different vehicles from said origin to saiddestination.
 78. A lane-level vehicle routing and navigation methodcomprising: performing, in a simulation engine, at intervals of up to 1second, microsimulation-based dynamic traffic assignment for individualvehicles in a traffic stream, along candidate paths determined bylink-level optimization, to predict travel times and to make lane userecommendations; inputting real-time traffic condition data; andproviding said lane use recommendations, from said performingmicrosimulation-based dynamic traffic assignment in said simulationengine, to a vehicle operator via a user communication device; wherein:said performing bases said simulation at least in part on said real-timetraffic condition data.
 79. The lane-level vehicle routing andnavigation method of claim 78 wherein said performing comprisesperforming said microsimulation-based dynamic traffic assignment atintervals of between 0.1 second and 1 second.
 80. The lane-level vehiclerouting and navigation method of claim 79 wherein said performingcomprises performing said microsimulation-based dynamic trafficassignment at intervals of 0.1 second.
 81. The lane-level vehiclerouting and navigation method of claim 78 wherein said real-time trafficcondition data are available for at least one of (a) one or morevehicles, or (b) one or more road segments.
 82. The lane-level vehiclerouting and navigation method of claim 78 wherein said real-time trafficcondition data are available at lane level.
 83. A lane-level vehiclerouting and navigation apparatus for a self-driving automated vehicle,the apparatus comprising: a simulation module that performsmicrosimulation-based dynamic traffic assignment for individual vehiclesin a traffic stream to predict travel times; a link-level optimizer thatdetermines candidate paths based on link travel times predicted by saidsimulation module; and a lane-level route optimizer that evaluatespredicted conditions along candidate paths from an origin to adestination as determined by said simulation module, and determinesrecommended lanes to use and the associated lane-level maneuvers alongthe candidate paths; wherein: said self-driving automated vehicle makeslane changes based on lane-use recommendations from said lane-levelroute optimizer.
 84. The lane-level vehicle routing and navigationapparatus for a self-driving automated vehicle of claim 83 wherein: saidsimulation module performs multiple simulation runs for at least some ofsaid candidate paths as part of said microsimulation-based dynamictraffic assignment; and said link-level optimizer is utilized toidentify time-efficient and reliable routes and route guidance.
 85. Thelane-level vehicle routing and navigation apparatus for a self-drivingautomated vehicle of claim 83 wherein said lane-level route optimizertakes account of the operation of traffic controls along the candidatepaths.
 86. The lane-level vehicle routing and navigation apparatus for aself-driving automated vehicle of claim 85 wherein said operation ofsaid traffic controls is simulated by said simulation module.
 87. Thelane-level vehicle routing and navigation apparatus for a self-drivingautomated vehicle of claim 85 wherein said operation of said trafficcontrols is evaluated based on real-time traffic control data.
 88. Thelane-level vehicle routing and navigation apparatus for a self-drivingautomated vehicle of claim 83 further comprising: inputs for real-timetraffic condition data; wherein: said simulation module bases saidmicrosimulation-based dynamic traffic assignment at least in part onsaid real-time traffic condition data.
 89. The lane-level vehiclerouting and navigation apparatus for a self-driving automated vehicle ofclaim 83 wherein said lane-level optimizer takes account of futuredownstream lane conditions.
 90. The lane-level vehicle routing andnavigation apparatus for a self-driving automated vehicle of claim 83wherein models used in said microsimulation, and said lane-leveloptimizer, take account of effects of obstacles to traffic flow.
 91. Thelane-level vehicle routing and navigation apparatus for a self-drivingautomated vehicle of claim 90 wherein said obstacles to traffic flowcomprise one or more of (a) work zones, or (b) traffic accidents. 92.The lane-level vehicle routing and navigation apparatus for aself-driving automated vehicle of claim 83 wherein said simulationmodule is at least one of (a) multi-threaded, or (b) distributed.